Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

https://doi.org/10.1038/s41598-024-67299-w

Multi standard citation

APAHarvardMLAVancouverChicagoIEEE

Patrick Ongomhttps://orcid.org/0000-0002-5303-3602

Edward Kanjuhttps://orcid.org/0000-0002-0413-1302

Kayondo Siraj Ismailhttps://orcid.org/0000-0002-3212-5727

Ismail Rabbihttps://orcid.org/0000-0001-9966-2941

https://doi.org/10.1038/s41598-024-67299-w

Biotech and Plant Breeding

Agronomy; Cassava; Food Security; Plant Breeding; Plant Production

Temperature; Viscosity; Forecasting; Phenotypes; Cassava; Calibration; Tanzania

Africa; East Africa

Tanzania

Eastern Africa Hub; Headquarters and Western Africa Hub

Scientific Reports